Transcript
Page 1: Autonomous Car-Control Mechanism - Patent iNSIGHT Pro Car-Control... · An autonomous car (driverless car, self-driving car, robotic car) is a vehicle that is capable of sensing its

An autonomous car (driverless car, self-driving car, robotic car) is a vehicle that is capable of sensing its environment and navigating without human input.

Autonomous Car-Control Mechanism

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Table of Contents

Introduction ............................................................................................................................................ 3

Patent Search Strategy ........................................................................................................................... 4

Evolution of Driverless Cars over the Years ............................................................................................ 6

Technical Segmentation (Patent Categorization) ................................................................................... 7

Filing Trend ............................................................................................................................................. 8

Top Companies ....................................................................................................................................... 9

Research activity around the world ...................................................................................................... 10

Companies - Key Statistics .................................................................................................................... 11

Inventor - Key Statistics ........................................................................................................................ 14

Company activity across Control Mechanisms ..................................................................................... 17

Company activity across Anti-Collision Systems ................................................................................... 18

Company activity across Braking Mechanisms .................................................................................... 19

Technology Landscape for different mechanisms used by a driverless car .......................................... 20

Universities and their research interest across different technology segmentations .......................... 21

Analysis for key mechanisms used by driverless cars ........................................................................... 22

Appendix: Search Strings Used for Categorization ............................................................................... 24

Mechanisms:......................................................................................................................................... 24

Anti-Collision System: ........................................................................................................................... 24

Braking Control Mechanism: ................................................................................................................ 24

Definitions of Classes referred to in search query ................................................................................ 25

Summary............................................................................................................................................... 26

Sources & References ........................................................................................................................... 28

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Introduction

An autonomous car (driverless car, self-driving car, robotic car) is a vehicle that is capable of sensing its environment and navigating without human input. Autonomous vehicles detect surroundings using radar, LIDAR, GPS, Odometry, and computer vision. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage. Autonomous cars have control systems that are capable of analyzing sensory data to distinguish between different cars on the road, which is very useful in planning a path to the desired destination General Motor's Cadillac division expects to produce partially autonomous cars on a large scale by 2015, and the car manufacturer also predicts it will have fully autonomous cars available by the end of the decade. Audi and BMW have also shown self-driving car concepts, with the former working with Stanford University to pilot a modified TT up Pikes Peak. A fully autonomous car can be defined as a car which is able to perceive its environment, decide what route to take to its destination, and drive it. The development of this could allow significant changes to travel – without the need for human supervision or operation, everyone in the car could be a passenger, or it could even drive with no occupants at all. This could allow productivity and leisure time to be reclaimed from commutes, transport accessibility to be widened for those previously unable to drive, and greater traffic efficiency. Autonomous cars could have a positive environmental impact. Driving at more consistent speeds, with less accelerating and braking, as well as more efficiently chosen routes could result in lower carbon emissions from driving. This report analyzes the innovation in the field of autonomous cars with respect to the different control mechanisms, braking technologies and anti-collision systems used.

NOTE: All analysis in this report has been done on INPADOC Families (Extended Families) and so the data in the charts should be construed accordingly.

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Patent Search Strategy

Using PatSeer following search query was used to create patent set. TAC- Title, Abstract, Claims TACD- Full Text

IC- International Class CPC- Cooperative Patent Classification

(((TAC: (((autonomous* OR driver_less OR self_driv* OR autonomic* OR robotic) w3 (car OR vehicle)) OR ((collision_avoid* OR pre_crash system OR parking_assist* OR adapt*_brak* OR control*_brak* OR automatic*_brak* OR cruise_control*) AND (vehicle OR car)) OR (vehicle controls ECU)) AND (IC:(B60W* OR B62D101/00 OR B62D109/00 OR B62D133/00 OR G07B15/02 OR G08G1/0955 OR G08G1/16 OR B60R21/00 OR B60K31/00 OR G01S13/93 OR B60T7/22 OR B62D6/00 OR B60T7/12 OR G05D1/0* OR B60R21/01 OR B60R21/013 OR B60T8/32 OR B62D101/00 OR B62D113/00 OR G01C22/00 OR G08G1/16 OR G01S17* OR G01C21/00 OR G08G1* OR B60L11/14 OR B60L15/20 OR B60L15/32 OR B60L15/40 OR B60L15/42 OR B60L11/18 OR B60L3* OR B60L7*) OR CPC:(B60K31* OR B60L11/14 OR B60L11/18 OR B60L15/20 OR B60L15/32 OR B60L15/40 OR B60L15/42 OR B60L3* OR B60L7* OR B60R19/483 OR B60R21/00 OR B60R21/01 OR B60R21/013 OR B60T7/12 OR B60T7/22 OR B60T8/32 OR B60W* OR B62D101/00 OR B62D109/00 OR B62D113/00 OR B62D133/00 OR B62D15* OR B62D6/00 OR G01C21* OR G01C22/00 OR G01S13/93 OR G01S13/931 OR G01S15/88 OR G01S15/93 OR G01S15/931 OR G01S17* OR G01S2013/9314 OR G01S2013/9317 OR G01S2013/9321 OR G01S2013/9325 OR G01S2013/9332 OR G01S2013/9339 OR G01S2013/9342 OR G01S2013/9346 OR G01S2013/935 OR G01S2013/9353 OR G01S2013/9357 OR G01S2013/936 OR G01S2013/9364 OR G01S2013/9367 OR G01S2013/9371 OR G01S2013/9375 OR G01S2013/9378 OR G01S2013/9382 OR G01S2013/9385 OR G01S2013/9389 OR G01S2015/938 OR G05D1/00 OR G05D1/0005 OR G05D1/0011 OR G05D1/0016 OR G05D1/0022 OR G05D1/0027 OR G05D1/0033 OR G05D1/0038 OR G05D1/0044 OR G05D1/005 OR G05D1/0055 OR G05D1/0061 OR G05D1/0066 OR G05D1/0072 OR G05D1/0077 OR G05D1/0088 OR G05D1/0094 OR G05D1/02 OR G05D1/021 OR G05D1/0212 OR G05D1/0214 OR G05D1/0217 OR G05D1/0219 OR G05D1/0221 OR G05D1/0223 OR G05D1/0225 OR G05D1/0227 OR G05D1/0229 OR G05D1/0231 OR G05D1/0234 OR G05D1/0236 OR G05D1/0238 OR G05D1/024 OR G05D1/0242 OR G05D1/0244 OR G05D1/0246 OR G05D1/0248 OR G05D1/0251 OR G05D1/0253 OR G05D1/0255 OR G05D1/0257 OR G05D1/0259 OR G05D1/0261 OR G05D1/0263 OR G05D1/0265 OR G05D1/0268 OR G05D1/027 OR G05D1/0272 OR G05D1/0274 OR G05D1/0276 OR G05D1/0278 OR G05D1/028 OR G05D1/0282 OR G05D1/0285 OR G05D1/0287 OR G05D1/0289 OR G05D1/0291 OR G05D1/0293 OR G05D1/0295 OR G05D1/0297 OR G05D1/03 OR G05D1/0891 OR G05D2201/02 OR G05D2201/0212 OR G05D2201/0213 OR G07B15/02 OR G08G1*) ) AND NOT TACD:(saddle*_vehicle OR aerial_vehicle OR underwater OR UAV OR AUV OR ROV OR tripod OR autonomous_underwater_vehicles OR unmanned_underwater_vehicles)) AND TAC:(vehicle_propel* OR (adapt* w2 control*) OR vehicle_speed OR vehicle_steer* OR vehicle_stability OR (collision* w2 (detect* OR response*)) OR ((adaptive OR automatic*) w2 brak*) OR ((cruise* OR park*) w2 assist*) OR warning_system* OR alarm_system* OR (lane* w2 keep*) OR lane_keep* OR automatic_park*)))

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The publications included in the report are updated as of 28th March, 2016

The query was directed to search through the title, abstract and claims. The individual results were

collapsed to one publication per family which was then exported from PatSeer and imported in Patent iNSIGHT Pro.

After reviewing few results across different publication years, we came across some similar but irrelevant terms which we then excluded from the data set manually.

Result set of 7724 records was analyzed using the software.

Class Definitions are mentioned at the end of the report under Appendix section.

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Evolution of Driverless Cars over the Years

1920-1930

•In 1925, Houdina Radio Control demonstrated the radio-controlled driverless car "linrrican Wonder" on New York City streets, traveling up Broadway and down Fifth Avenue through the thick of the traffic jam

•An early representation of an automated guided car was Norman Bel Geddes's Futurama exhibit sponsored by General Motors at the 1939 World's Fair, which depicted radio-controlled electric cars that were propelled via electromagnetic fields provided by circuits embedded in the roadway.

1950-1980

•Special radio receivers and audiovisual warning devices,Electronically Controlled Highways, Relaying Computer Messages

•Laser Radar, Computre Vision, Autonomous Robotic Control,demonstration of the first off-road map and sensor-based autonomous navigation on the Automatic Land Vehicle(ALV).

1990-2010

•Demonstrations of close-headway platooning intended to operate in segregated traffic, as well as "free agent" vehicles intended to operate in mixed traffic

•autonomous driving in free lanes, convoy driving, and lane changes with autonomous passing of other cars

•Carnegie Mellon University's Navlab project completed a 3,100 miles (5,000 km) cross-country journey, of which 98.2% was autonomously controlled, dubbed "No Hands Across America

•RFID Tags and Real Time Control Systems

•Artificial Intelligence and emergance of LIDAR technology ,Sensors, Global Positioning Systems, 3-D Maps, Stereo vision and Mono vision Camaras

2011-2016

•Freie Universität Berlin developed two autonomous cars to drive in the innercity traffic of Berlin in Germany

•VisLab conducted another pioneering test of autonomous vehicles, during which a robotic vehicle drove in downtown Parma with no human control, successfully navigating roundabouts, traffic lights, pedestrian crossings and other common hazards

•Infiniti Q50 uses cameras, radar and other technology to deliver various lane-keeping, collision avoidance and cruise control features

•First self-driving vehicle to be available for commercial sale

• Google announced plans to unveil 100 autonomous car prototypes built from scratch inside Google's secret X lab

•Tesla Motors announced its first version of AutoPilot. Model S cars equipped with this system are capable of lane control with autonomous steering, braking and speed limit adjustment based on signals image recognition. The system also provide autonomous parking and is able to receive software updates to improve skills over time

•A car designed by Delphi Automotive became the first automated vehicle to complete a coast-to-coast journey across North America

•Google announced that the test vehicles in its driverless car project had been involved in 14 minor accidents since the project's inception in 2009.All of the accidents were caused by humans driving other cars, and that 11 of the mishaps were rear-end collisions

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Technical Segmentation (Patent Categorization)

Control Mechanisms Anti-Collision System Breaking Mechanism

Blind Spot Detection

Cruise Control

Lane Keeping

LIDAR

Parking Assist

Propulsion

Stability

Steering

Traffic Jam Assist

Warning System

Wheel Speed Sensor

Collision Detection

Obstacle Detection

Adaptive Braking

Automatic Braking

The categorization involved defining a search strategy for each topic and then conducting the search using the Advanced Searching capability in Patent iNSIGHT Pro. Details of search strings used for each category are given in Appendix.

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Filing Trend The chart below shows number of filings for driverless cars during the last 30 years. The number of filings has steadily increased since 2000 with a minor fall in the number of filings during 2008-2009. The overall trend has seen constant rise in the number of filings with sudden rise from the year 2011. From 40 filings in the year 1986 to nearly 700 in 2013, driverless cars have seen tremendous increase.

It’s clear the current activity around these technologies is likely to continue seeing more innovation in the near future.

How we did it?

Once the patents were populated in Patent iNSIGHT Pro, the publication trend chart was generated on a single click using the dashboard tool.

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Top Companies

The top companies in Autonomous Car are:

How we did it?

Once the patents were populated in Patent iNSIGHT Pro, the assignee clean- up tools were used to normalize the names. Different cleanup tools were leveraged: • To locate assignees for unassigned records • To clean up records having multiple assignees • To locate the correct assignee names for US records using the US assignments database • To merge assignees that resulted from a merger or acquisition or name change. The dashboard tool within Patent iNSIGHT Pro was used to find the top 20 assignees within the given patent set. A visual graph was created based on the results of the top assignees with the number of patents alongside each one. The complete Assignee table in available in the following Excel file: http://www.patentinsightpro.com/techreports/0416/List%20of%20Assignees.xls

11. FORD GLOBAL TECH LLC 12. HYUNDAI MOBIS CO LTD 13. AISIN SEIKI CO LTD 14. VOLKSWAGEN AG 15. CONTINENTAL TEVES AG 16. BMW AG 17. FUJI HEAVY INDUSTRIES 18. MITSUBISHI MOTORS 19. HITACHI LTD 20. VALEO SCHALTER UND SENSOREN GMBH

1. TOYOTA MOTOR CORP 2. NISSAN MOTOR CO LTD 3. ROBERT BOSCH GMBH 4. HONDA MOTOR CO LTD 5. DENSO CORP 6. GM GLOBAL TECHNOLOGIES 7. HYUNDAI MOTOR CO 8. MANDO CORP 9. MAZDA MOTORS 10. DAIMLER AG

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Research activity around the world The below map represents the geographical filing of patents relating to driverless car technology. The table below ranks top priority countries and helps provide an indication of where innovation in this area is originating. Toyota which is the leading entity in driverless car technology makes Japan the leading country in this field with 3089 families followed by Germany (1432) and US (915). The strength of the coloring represents the proportion of patent publications.

How we did it? The map was generated using the Priority country coverage map option provided in the dashboard tool within Patent iNSIGHT Pro.

Country Code

Total

JP 3089

DE 1432

US 915

CN 843

KR 709

GB 180

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Companies - Key Statistics

Here we summarize key parameters of Top 15 companies such as filing trend, Top inventors in each company and Coverage of underlying patent families

Assignee Total No. of

Records

Avg. No. of Fwd Cites per

Patents

Key Inventor (Top 5)

Co-Assignees

Coverage(Includes families)

US EP WO JP DE FR CN KR IN

TOYOTA MOTOR CORP

546 (7.1%)

4.13 ENDO TOMOHIKO

KUBOTA YUICHI

SATONAKA HISASHI MAKINO YASUSHI IWAKIRI

HIDEYUKI

AISIN SEIKI CO LTD

ADVICS CO LTD DENSO CORP

TOYOTA CENTRAL

RESEARCH & DEVELOPMENT

LAB INC AISIN AW CO

LTD

195 116 131 502 106 4 123 36 11

NISSAN MOTOR CO LTD

454 (5.9%)

6.11 SETO YOJI YAMAMURA YOSHINORI

TAMURA MINORU

INOUE HIDEAKI SUZUKI

TATSUYA

JATCO CO LTD NAT INST OF ADV IND & TECHNOL

UNIV KYUSHU

148 76 20 426 82 1 35 8 1

ROBERT BOSCH GMBH

429 (5.6%)

4.18 WINNER HERMANN

NIEMZ VOLKER UHLER

WERNER SCHNEIDER

MARCUS SCHERL

MICHAEL

ROBERT BOSCH ENG & BUSINESS SOLUTIONS LTD

225 245 210 148 393 48 109 31 16

HONDA MOTOR CO LTD

194 (2.5%)

3.97 SEKINE HIROSHI

URAI YOSHIHIRO

SAKAI KATSUHIRO

ARAI TOSHIAKI SHIMIZU YASUO

No Co-Assignee Present

69 28 11 181 32 0 23 5 1

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DENSO CORP 191 (2.5%)

5.17 ISOGAI AKIRA TERAMURA

EIJI TSURU

NAOHIKO ITO YOSUKE

SHIMIZU HIROAKI

NIPPON SOKEN INC

TOYOTA MOTOR CORP

ADVICS CO LTD HINO MOTORS

LTD DENSO IT LAB

INC

71 13 6 180 47 3 24 4 0

GM GLOBAL TECHNOLOGY

OPERATIONS INC

186 (2.4%)

7.19 LEE JIN-WOO ZAGORSKI

CHAD T CHEN SHIH-

KEN LITKOUHI BAKHTIAR

BRIAN MOSHCHUK NIKOLAI K

No Co-Assignee Present

168 7 13 5 141 1 107 0 7

HYUNDAI MOTOR CO LTD

166 (2.1%)

1.3 LEE CHAN KYU

KIM JONG CHUL

KIM SANG JOON

KIM JEE YOUNG

JEON JAE HWAN

KIA MOTORS CORP

52 2 0 23 29 0 35 162 0

MANDO CORP 152 (2%)

0.85 NOH TAE BONG

KIM JAE SUK YOU KWAN

SUN YOO JE HONG

PARK MAN BOK

No Co-Assignee Present

29 1 0 1 28 0 23 152 0

MAZDA MOTORS CORP

148 (1.9%)

7.27 ADACHI TOMOHIKO YAMAMOTO YASUNORI

NIIBE TADAYUKI

KAMIMURA HIROKI

ISHIKAWA TOSHIHIRO

NALDEC KK

30 5 1 140 19 0 1 3 0

DAIMLER AG 147 (1.9%)

6.08 STEINER MANFRED

KNOFF BERND RUMP

SIEGFRIED ECKL

ALBRECHT REUTTER DOMINIC

No Co-Assignee Present

62 19 24 69 121 32 4 0 0

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FORD GLOBAL TECH LLC

141 (1.8%)

6.39 ENGELMAN GERALD H

TELLIS LEVASSEUR

PILUTTI THOMAS EDWARD

LU JIANBO STEFAN

FREDERIC

No Co-Assignee Present

129 20 5 6 89 0 75 0 0

HYUNDAI MOBIS CO LTD

139 (1.8%)

0.18 LEE SEONG SOO

KIM IL HAN KIM HYE RIM KIM JEONG

KU HAM JUN HO

No Co-Assignee Present

19 3 0 0 8 0 53 137 0

AISIN SEIKI CO LTD 124 (1.6%)

6.66 WATANABE KAZUYA

SATONAKA HISASHI IWATA

YOSHIFUM ENDO

TOMOHIKO TANAKA YUU

TOYOTA MOTOR CORP

67 38 23 114 31 2 29 10 0

VOLKSWAGEN AG 104 (1.3%)

6.78 WUTTKE ULRICH HUEGER PHILIPP ROHLFS

MICHAEL TERKES

MEHMET MAI RUDOLF

AUDI AG VALEO

SCHALTER UND SENSOREN

GMBH

26 40 24 6 94 1 15 4 0

CONTINENTAL TEVES AG & CO

OHG

100 (1.3%)

3.57 STAEHLIN ULRICH ECKERT ALFRED

GRONAU RALPH LUEKE

STEFAN BAYER

RONALD

No Co-Assignee Present

48 54 60 28 83 0 17 23 0

How we did it?

From the Assignee 360° report options, we selected Top 15 Assignees and the different pieces of information we wanted to include in the singular display and then ran the report. The generated report was then exported to Excel using the option provided for the same.

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Inventor - Key Statistics Here we summarize key parameters of Top 15 Inventors such as filing trend, key associated companies and top 5 co-inventors.

Inventor Total No. of

Records

Avg. No. of Fwd Cites per

Patents

Filing Year Range

Key Assignees (Top 5)

Co-Inventors

ENDO TOMOHIKO

47 (0.6%)

6.3 2003

- 2010

TOYOTA MOTOR

CORP AISIN SEIKI

CO LTD

KUBOTA YUICHI IWAKIRI HIDEYUKI

SATONAKA HISASHI KATAOKA HIROAKI KAWAKAMI SEIJI

SATONAKA HISASHI

41 (0.5%)

6.51 1995

- 2013

TOYOTA MOTOR

CORP AISIN SEIKI

CO LTD

ENDO TOMOHIKO KATAOKA HIROAKI KAWAKAMI SEIJI

IWAKIRI HIDEYUKI IWATA YOSHIFUMI

KUBOTA YUICHI

37 (0.5%)

6.86 1998

- 2013

TOYOTA MOTOR

CORP AISIN SEIKI

CO LTD

ENDO TOMOHIKO IWAKIRI HIDEYUKI

SATONAKA HISASHI KATAOKA HIROAKI KAWAKAMI SEIJI

SETO YOJI 36

(0.5%) 7.06

1995 -

2008

NISSAN MOTOR CO

LTD

YAMAMURA YOSHINORI NAKAMURA MASAHIDE

TAMURA MINORU INOUE HIDEAKI

KOBAYASHI YOSUKE

SHIMAZAKI KAZUNORI

34 (0.4%)

5.97 2001

- 2010

TOYOTA IND CORP

TOYOTA MOTOR

CORP LOREAL SA

KIMURA TOMIO YAMADA SATOYUKI

NAKASHIMA YUTAKA HIKA KOJI

YAMADA SATOSHI

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WINNER HERMANN

34 (0.4%)

6.24 1997

- 2011

ROBERT BOSCH GMBH

CONTINENTAL TEVES AG &

CO OHG VOLKSWAGE

N AG UNIV

DARMSTADT TECH

HONDA MOTOR CO

LTD

HELLMANN MANFRED UHLER WERNER IRION ALBRECHT

LUEDER JENS WEILKES MICHAEL

KIMURA TOMIO

32 (0.4%)

6.25 2001

- 2010

TOYOTA IND CORP

TOYOTA MOTOR

CORP

SHIMAZAKI KAZUNORI YAMADA SATOYUKI

NAKASHIMA YUTAKA HIKA KOJI HIGA KOJI

IWAKIRI HIDEYUKI

31 (0.4%)

6.81 2003

- 2008

TOYOTA MOTOR

CORP AISIN SEIKI

CO LTD

ENDO TOMOHIKO KUBOTA YUICHI

SATONAKA HISASHI KATAOKA HIROAKI KAWAKAMI SEIJI

KAWAKAMI SEIJI

28 (0.4%)

7.64 1993

- 2007

TOYOTA MOTOR

CORP AISIN SEIKI

CO LTD

KATAOKA HIROAKI ENDO TOMOHIKO

SATONAKA HISASHI IWATA YOSHIFUMI

IWAZAKI KATSUHIKO

OKUYAMA HIROKAZU

28 (0.4%)

2.71 1997

- 2008

HINO MOTORS LTD

EZOE TOSHIKI OKAMOTO KOICHI ICHINOSE SUNAO

NARATA SHUJI KAGAWA MASAKATSU

EZOE TOSHIKI

27 (0.3%)

1.93 2006

- 2014

HINO MOTORS LTD

ICHINOSE SUNAO OKUYAMA HIROKAZU

OKAMOTO KOICHI NARATA SHUJI

HOKARI SATOSHI

KATAOKA HIROAKI

27 (0.3%)

7.89 2002-2007

TOYOTA MOTOR

CORP AISIN SEIKI

CO LTD

KAWAKAMI SEIJI ENDO TOMOHIKO

SATONAKA HISASHI IWATA YOSHIFUMI

IWAZAKI KATSUHIKO

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IWATA YOSHIFUMI

26 (0.3%)

7.96 2001-2007

AISIN SEIKI CO LTD TOYOTA MOTOR

CORP

ENDO TOMOHIKO KATAOKA HIROAKI KAWAKAMI SEIJI

SATONAKA HISASHI TANAKA YUU

YAMAMURA YOSHINORI

26 (0.3%)

10.5 1997-2007

NISSAN MOTOR CO

LTD

SETO YOJI TAMURA MINORU

NAKAMURA MASAHIDE TAKAHASHI MASAKI KOBAYASHI YOSUKE

JOHANSSON OSKAR

25 (0.3%)

0.36 2010-2013

SCANIA CV AB

ROOS FREDRIK SÖDERGREN MARIA

SOEDERGREN MARIA ÖGREN MIKAEL

EVALDSSON MARTIN

How we did it?

From the Inventor 360° report options, we selected the different pieces of information we wanted to include in the singular display and then ran the report. The generated report was then exported to Excel using the option provided for the same.

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Company activity across Control Mechanisms

The chart below shows research activity of companies across different Control mechanisms used in driverless car

Toyota has the most number of records for Parking Assist followed by Robert Bosch and Nissan Motors

GM Global has the maximum number of records for Lane Keeping and Warning Systems

How we did it? First various mechanisms used for driverless cars were identified by manual research. Then by using a combination of semantic analysis tools such as clustering tools and searching tools available in Patent iNSIGHT Pro, records were categorized under different mechanisms. A co-occurrence matrix was generated using the co-occurrence analyzer to map the different mechanisms with assignees. The matrix was filtered for the top 20 assignees and was converted into bubble column chart using the option provided in software for the same.

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Company activity across Anti-Collision Systems

The chart below shows research activity of companies across the types of anti-collision systems used

Collision Detection has more number of records filed as compared to Obstacle Detection

Toyota has the largest number of patents pertaining to Collision Detection followed by Nissan

How we did it? First various types of anti collision systems were identified by manual research. Then by using a combination of semantic analysis tools such as clustering tools and searching tools available in Patent iNSIGHT Pro, records were categorized under various anti collision systems. A co-occurrence matrix was generated using the co-occurrence analyzer to map the anti collision systems with assignees. The matrix was filtered for the top 20 assignees and types and converted into Stacked Column chart using the option provided in software for the same.

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Company activity across Braking Mechanisms

The chart below shows research activity of companies across different braking mechanisms used in a driverless car

Automatic Braking seems to be the most widely used braking mechanism with nearly 2100 families

Toyota lies at the top for patents relating to automatic braking closely followed by Nissan and Robert Bosch

Nissan and Hino have some research interest in adaptive braking

How we did it? First various braking mechanisms were identified by manual research. Then by using a combination of semantic analysis tools such as clustering tools and searching tools available in Patent iNSIGHT Pro, records were categorized under different braking mechanisms. A co-occurrence matrix was generated using the co-occurrence analyzer to map the different braking mechanisms and assignees. The matrix was filtered for the top 20 assignees and was converted into Stacked Column chart using the option provided in software for the same.

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Technology Landscape for different mechanisms used by a driverless car The contour map below represents key concepts for different companies across various mechanisms used in a driverless car. Clusters for Cruise Control and Propulsion and the records relating speed of the vehicle namely Wheel Speed Sensor and Steering are close to each other as there is high degree of relevance between the records present in those types of methods. The patents represented by dots were coloured by company.

How we did it? The VizMAP tool in Patent iNSIGHT Pro was used for this analysis. First the clusters for different mechanisms were loaded on the map. They were analyzed on basis of their contextual similarity using title, abstract and claims as Text and technology as UDC from the ‘Context mode’ option. We removed unrelated patents using the “Hide Unrelated records” option and one patent assignee using the options available in VizMAP.

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Universities and their research interest across different technology segmentations In the map, each company is connected to particular technology area through links whose thickness and color intensity is directly proportional to the number of records relating them. The number (in red) next to each line represents the number of records held by Universities present in a particular technology area. It can be seen that Univ Changan is mainly into Automatic Braking and Univ Jiangsu focuses more on Parking Assist as compared to other segmentations.

How we did it? We first filtered all the universities having research interest in driverless car technology from ‘Analyze by’ option available in VIZMAP. These were then correlated with all the technological segmentations and a correlation map was generated. Inter member links were hidden using the ‘Hide Member Links’ option available.

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Analysis for key mechanisms used by driverless cars This section focuses on key mechanisms used by various companies.

Parking Assist

Cruise Control

Lane Keeping

LIDAR The pie chart below displays Top 10 Companies in the field of Parking Assist (1504 Families). Some of the companies which are a part of this list are Toyota, Robert Bosch, Hyundai. The pie chart below displays Top 10 Companies in the field of Cruise Control (500 Families). Some of the companies which are a part of this list are Nissan, Denso, and Toyota, etc.

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The pie chart below displays Top 10 Companies in the field of Lane Keeping (295 Families). Some of the companies which are a part of this list are Mando, Nissan, Robert Bosch. The pie chart below displays Top 10 Companies in the field of LIDAR (92 Families). Some of the companies which are a part of this list are GM Global, Volvo. Some of the lesser known companies Safemine AG, Magna are also a part of the top 10 filers for LIDAR. How we did it? We identified some key mechanisms used in driverless cars. These mechanisms were analyzed using co-occurrence analyzer with respect to assignees, the generated matrix was exported to excel and pie chart was generated for the same.

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Appendix: Search Strings Used for Categorization Mechanisms:

Mechanisms Search Query Results

Blind Spot Detection (TAC) contains ((blind* w/2 (detect* OR assist*)) OR BSD) 31

Cruise Control (TAC) contains ((vehicle* OR car*) w/3 (speed* AND cruis*) OR CCVS OR crusing OR (cruise* w/2 (control* OR assist*)) OR ACC)

500

Lane Keeping (TAC) contains ((lane* w/2 (keep* OR chang* OR depart*)) OR LKAS) 295

LIDAR (TAC) contains ((Light* w/3 Rang*) OR LIDAR) 92

Parking Assist (TAC) contains ((park* w/2 assist*) OR (self w/2 park*) 1504

Propulsion (TAC) contains ((vehicle* OR car*) w/3 (propul* OR propeller* OR propelled OR propel*))

177

Stability (TAC) contains ((vehicle* OR car* OR electronic* OR dynamic* OR electric*) w/3 (stability OR stabilization OR stabilized OR stabilizing) OR ESC OR ESP OR DSC OR skidding OR (vehicle* w/2 dynamic*))

344

Steering (TAC) contains ((vehicle* OR car*) w/3 (steer*) OR oversteer* OR (active* w/2 steer*))

866

Traffic Jam Assist (TAC) contains (traffic* w/3 (assist* OR recogni*) ) 43

Warning System (TAC) contains ((warn* OR alarm*) w/2 system*) 318

Wheel Speed Sensor (TAC) contains (wheel* w/3 speed w/3 sens*) 221

Anti-Collision System:

Anti-Collision System

Search Query Results

Collision Detection (TAC) contains ((collision*) w/3 (detect* OR avoid*) OR (time* w/2 colli*)) 1063

Obstacle Detection (TAC) contains ((obstacle*) w/3 (detect* OR avoid*)) 632

Braking Control Mechanism: Methods Search Query Results

Adaptive Braking (TAC) contains ((adaptive* w/3 brak*) OR (anti* w/2 skid*) OR (anti* w/2 lock*))

82

Automatic Braking (TAC) contains (automatic* w/2 brak*) 2118

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Definitions of Classes referred to in search query

IPC:

IPC Description B60W Conjoint control of vehicle sub-units of different type or different function B62D 101/00 Road speed B62D 109/00 Presence, absence or inactivity of driver or operator B60L 7/00 Electrodynamic brake systems for vehicles in general B60L 3/00 Electric devices on electrically-propelled vehicles for safety purposes B60L 11/18 Electric propulsion with power supplied within the vehicle -using power supplied from

primary cells, secondary cells, or fuel cells B60L 15/42 Adaptation of control equipment on vehicle for actuation from alternative parts of the

vehicle B60L 15/40 Adaptation of control equipment on vehicle for remote actuation from a stationary

place B60L 15/32 Control or regulation of multiple-unit electrically-propelled vehicles B60L 15/20 Methods, circuits or devices for controlling the propulsion of electrically-propelled

vehicles B60L 11/14 Electric propulsion with power supplied within the vehicle with provision for direct

mechanical propulsion G08G 1/0955 Traffic control systems for road vehicles G08G1/16 Anti-collision systems B60K 31/00 Vehicle fittings, acting on a single sub-unit only, for automatically controlling vehicle

speed

B60T 7/22 Brake-action initiating means B62D 113/00 Position of parts of the steering mechanism B60R21/01 Electrical circuits for triggering safety arrangements in case of vehicle accidents or

impending vehicle accidents B60T8/32 Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-

surface conditions responsive to a speed condition, e.g. acceleration or deceleration B60R21/013 means for detecting collisions, impending collisions or roll-over B60R 21/01 Electrical circuits for triggering safety arrangements in case of vehicle accidents or

impending vehicle accidents G01S 17/00 Systems using the reflection or reradiation of electromagnetic waves other than radio

waves, e.g. lidar systems G01C 21/00 Navigational instruments for measuring distance traversed on the ground by a vehicle G01C 21/26 Navigational instruments adapted for navigation in a road network G01C 22/00 Measuring distance traversed on the ground by vehicles, persons, animals or other

moving solid bodies, e.g. using odometers or using pedometers G01S 13/93 Systems using the reflection or reradiation of radio waves, e.g. radar systems for anti-

collision purposes G05D1/0 Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g.

automatic pilot (radio navigation systems or analogous systems using other waves G01S) B60T 7/12 automatic initiation; for initiation not subject to will of driver or passenger B62D 6/00 Arrangements for automatically controlling steering depending on driving conditions

sensed and responded to G08G 1/00 Traffic control systems for road vehicles B60R 21/00 Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or

pedestrians in case of accidents or other traffic risks B62D 133/00 Trim or inclination, including road gradient B60L 11/14 Electric propulsion with provision for direct mechanical propulsion

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Summary This report categorizes and graphically analyzes research trends around the mechanisms, ant-collision system and braking technologies used by a driverless car from various perspectives and highlights the key companies involved. In the United States, the National Highway Traffic Safety Administration (NHTSA) has proposed a formal classification system: The following pertains to automated vehicles. Level 0: The driver completely controls the vehicle at all times. Level 1: Individual vehicle controls are automated, such as electronic stability control or automatic braking. Level 2: At least two controls can be automated in unison, such as adaptive cruise control in combination with lane keeping. Example: Tesla Model S Level 3: The driver can fully cede control of all safety-critical functions in certain conditions. The car senses when conditions require the driver to retake control and provides a "sufficiently comfortable transition time" for the driver to do so. Level 4: The vehicle performs all safety-critical functions for the entire trip, with the driver not expected to control the vehicle at any time. As this vehicle would control all functions from start to stop, including all parking functions, it could include unoccupied cars. Some of the driverless technologies which are already being used by semi-autonomous cars are parking assist, cruise control and automatic braking. Others such as blind spot detection, traffic jam detection are still in the early stages of development. Key factors that would impact producer deployment of autonomous vehicles include technological feasibility, digital infrastructure, producer liability, regulation, diverging business models, and profitability.

According to a new report covered by the IEEE, driverless car-compliant microcontroller and processor units will be a $500 million market by 2020, up from $69 million last year Looking at the pace at which records are being file in the field of driverless cars, we can have self driving cars on the roads in the near future. Some of the advantages of self driving vehicles are fewer accidents, congestion reduction and increased roadway capacity, reduction of physical read signage, smoother traffic and improved fuel efficiency

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About Patent iNSIGHT Pro

Patent iNSIGHT Pro™ is a comprehensive patent analysis platform that allows you to accelerate your time-to-decision from patent analysis activities. Designed from inputs by experienced patent researchers, Patent iNSIGHT Pro easily blends into your existing Research workflow. Patent iNSIGHT Pro is used by leading legal services, Pharmaceutical & biotech, electronics companies and research organization across US, Europe, South America and India with more than 400 end users. Patent iNSIGHT Pro is developed and marketed by Gridlogics, a research driven IT Company specializing in providing intellectual property analysis and visualization solutions to aid R&D and corporate strategy. Gridlogics is headquartered in Pune, India and has a sales presence in Delhi, Mumbai and USA. For more information: Visit us at: www.patentinsightpro.com Or call us at: 1-408-786-5524 Or mail us at:

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PatSeer is a global patent database that includes many advanced analysis, project management and collaboration capabilities making it unique from competitive solutions. With a clean and intuitive interface, PatSeer makes it very easy to conduct powerful analysis and deliver analyzed data. PatSeer creates a centralized work environment for a team to manage to search patent databases, work on patent data projects, carry out analysis and deliver insights. Coupled with powerful workflow management features, the platform can be customized to operate according to internal processes and requirements. PatSeer includes full text data of 27 most notable countries and bibliographic data for 104+ countries as part of INPADOC collection. PatSeer also has enhanced content such as normalized assignees for top 3000 companies, corporate tree for top 3000 corporate groups and a semantic index that helps you refine your search by giving meaningful suggestions. Patent PDFs, image mosaics, legal status and family of the document are integrated with rest of the content.

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Sources & References

en.wikipedia.org/wiki/History_of_autonomous_car en.wikipedia.org/wiki/Autonomous_car wired.co.uk/news/archive/2012-09/19/no-drivers-licences-by-2040 electronicproducts.com/Digital_ICs/Microprocessors_Microcontrollers_DSPs/Google_s_driverless_car_to_boost_revenue_for_semiconductors.aspx e2e.ti.com/blogs_/b/tilive/archive/2015/03/17/lidar-cameras-radars-action-the-road-to-autonomous-vehicles auto.howstuffworks.com/under-the-hood/trends-innovations/driverless-car2.htm ikhlaqsidhu.files.wordpress.com/2013/06/self_driving_cars.pdf googleblog.blogspot.in/2010/10/what-were-driving-at.html engadget.com/2012/12/03/volvo-self-driving-cars-2014/ en.wikipedia.org/wiki/Collision_avoidance_system en.wikipedia.org/wiki/Automatic_braking en.wikipedia.org/wiki/Anti-lock_braking_system en.wikipedia.org/wiki/Emergency_brake_assist en.wikipedia.org/wiki/Autonomous_cruise_control_system en.wikipedia.org/wiki/Cooperative_Adaptive_Cruise_Control en.wikipedia.org/wiki/Lidar forbes.com/sites/zacharyhamed/2015/01/21/driverless-stocks/#6f7ce88734f9 bankrate.com/finance/auto/companies-testing-driverless-cars-1.aspx


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